Correlating attributes of telecommunication network data is necessary for various analytics use-cases, such as root cause analysis, pro-active maintenance, etc. For instance, correlating key performance indicators (KPIs) and alarm data can help in identifying KPIs that contribute to each alarm.
Attribute correlation techniques can be broadly categorized into two categories: Direct (straight forward) correlation and Indirect (statistical) correlation. Direct (straight forward) correlation can be done by simply deciphering the mathematical formula used to compute various metrics (such as KPIs). Indirect (statistical) correlation can capture latent factors, and cannot be achieved by direct correlation technique. For instance, correlating network KPIs and user mean opinion score (MOS) should capture factors such as user interest for a service, mobility pattern, weather and so on. See e.g., Fabrigar et al. (1999), “Evaluating the use of exploratory factor analysis in psychological research,” Psychological Methods.